store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 1024, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 40, 'num_iterations': 20, 'no_cuda': False, 'name': 'bald_40_646208', 'seed': 646208, 'log_interval': 20, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[6758, 50944, 1716, 9430, 23995, 58590, 22149, 40463, 38789, 21770, 18197, 21632, 46845, 28496, 55699, 4042, 40083, 46767, 41875, 53645]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7284, 'nll': 1.8908481357574463}, 'chosen_samples': [29062, 27243, 41119, 5988, 49371, 47028, 7739, 59377, 8311, 6237, 51620, 49509, 28197, 39597, 1436, 53455, 28374, 25477, 48792, 8469, 30074, 42966, 41881, 20018, 21722, 6391, 3645, 50336, 39873, 55316, 31710, 57461, 42541, 40579, 24010, 33081, 5740, 15887, 2738, 22551], 'chosen_samples_score': ['1.0709321', '1.0715895', '1.07233', '1.0739543', '1.0760188', '1.0762227', '1.0785478', '1.0793324', '1.0768499', '1.0802562', '1.0865152', '1.0978885', '1.1116126', '1.083591', '1.1457169', '1.0911126', '1.0889144', '1.1639097', '1.115028', '1.1383936', '1.199914', '1.1076465', '1.1157991', '1.1282794', '1.1251053', '1.1210206', '1.118257', '1.1811919', '1.110745', '1.227211', '1.0984813', '1.0992054', '1.0941211', '1.1127374', '1.1872795', '1.1515472', '1.1314917', '1.0889547', '1.1292548', '1.0873566']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7566, 'nll': 1.2000101459503174}, 'chosen_samples': [58092, 31847, 43672, 25390, 28937, 20820, 57904, 41955, 48598, 44736, 37034, 53170, 58563, 17010, 41336, 57512, 55545, 54894, 30154, 4679, 12497, 51492, 13179, 48334, 34789, 40637, 11727, 20581, 45091, 44740, 52528, 27584, 45852, 2879, 27376, 11133, 41722, 46864, 52574, 25470], 'chosen_samples_score': ['0.8422609', '0.84236366', '0.8427489', '0.8508918', '0.8428901', '0.85018706', '0.8540586', '0.8537825', '0.8435444', '0.85222155', '0.8547992', '0.8512356', '0.8431331', '0.85006744', '0.8553598', '0.9321597', '0.8841841', '0.8956941', '0.979914', '0.8866393', '0.89175296', '0.8575994', '0.881063', '0.97653425', '0.8682876', '0.87360674', '0.8621555', '0.875771', '0.89349174', '0.8770453', '0.92578954', '0.8775044', '0.88984746', '0.8995548', '0.85593235', '0.9117203', '0.8563544', '0.8647718', '0.8851234', '0.87888145']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8208, 'nll': 1.128042946434021}, 'chosen_samples': [22633, 52862, 11626, 33388, 55055, 47753, 46580, 34574, 23461, 19590, 55839, 29037, 32427, 8678, 41960, 49671, 33593, 53286, 19942, 35073, 26203, 36415, 29900, 4792, 53530, 33358, 27294, 47115, 26842, 8799, 24310, 12196, 55591, 36582, 799, 23347, 6755, 19505, 21383, 1324], 'chosen_samples_score': ['0.95936435', '0.9599505', '0.9620573', '0.96405965', '0.96773887', '0.97001624', '0.9636691', '0.9669294', '0.9640117', '0.96803343', '0.9705008', '0.97394556', '0.977374', '1.021805', '0.9824737', '1.0096619', '1.1177685', '1.0320222', '1.1028843', '1.0340376', '0.9899364', '1.1030085', '1.0588791', '1.0279527', '1.0056541', '1.0128276', '1.0054536', '1.0047706', '1.030287', '1.0078208', '1.0099194', '1.0645959', '1.0089602', '1.0456568', '1.0140848', '0.9818792', '1.002763', '1.1353672', '0.99330443', '1.0955721']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8658, 'nll': 0.7567809993743897}, 'chosen_samples': [29633, 49809, 29320, 30884, 18739, 57211, 51173, 53344, 26315, 1661, 43852, 14649, 17870, 2184, 44202, 49499, 49859, 5152, 32411, 14749, 49491, 30418, 28454, 1148, 21242, 37794, 44326, 13714, 27477, 10924, 51644, 54994, 42746, 33364, 45732, 44753, 34708, 12647, 43602, 19478], 'chosen_samples_score': ['0.8206875', '0.8208036', '0.8219138', '0.8225973', '0.8253077', '0.8254712', '0.82381856', '0.82334936', '0.82814497', '0.8443242', '0.8648452', '0.9259618', '0.8435585', '0.90175265', '0.8472956', '0.8398302', '0.83408415', '0.8661103', '0.8469628', '0.89917064', '0.828272', '0.8943825', '0.8362747', '0.8335743', '0.9261543', '0.84426165', '0.8580983', '0.91120714', '0.8364483', '0.83914804', '0.83400935', '0.8559633', '0.8453034', '0.89077634', '0.85195446', '0.8444804', '0.84655255', '0.9337862', '0.830498', '0.8516648']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8956, 'nll': 0.6790449596405029}, 'chosen_samples': [45660, 24426, 34597, 54002, 33057, 50320, 14201, 57431, 37048, 1360, 52237, 41299, 30470, 18962, 5821, 12566, 53306, 34445, 24479, 38577, 58331, 3719, 45536, 13907, 44095, 5155, 11154, 31512, 45917, 31426, 22513, 21734, 41933, 21894, 29662, 47401, 23824, 28844, 15616, 20476], 'chosen_samples_score': ['0.9158441', '0.91920435', '0.92146707', '0.9178754', '0.91859317', '0.9163504', '0.9237712', '0.92450756', '0.9273941', '0.9375764', '0.93390435', '0.94191605', '0.92835313', '0.9375616', '0.9283769', '0.94360083', '0.940751', '0.9440541', '1.073885', '1.038389', '0.97127235', '0.9525831', '1.0258234', '1.0191729', '0.95341885', '1.1808197', '0.9837571', '1.0932422', '1.0258036', '0.9968715', '1.1122912', '0.97656304', '1.1693704', '0.9513315', '0.9729854', '0.97499216', '0.9594934', '0.9650288', '0.94774437', '0.9954849']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9291, 'nll': 0.4883266408920288}, 'chosen_samples': [45154, 8780, 56391, 8520, 26850, 47387, 38920, 52478, 1121, 22664, 57342, 59726, 40530, 8691, 6942, 55743, 6347, 22083, 28757, 43898, 17941, 48384, 6269, 20328, 41789, 44236, 11708, 59280, 2818, 51158, 9731, 16448, 12692, 10210, 50698, 32774, 36008, 6466, 10256, 31926], 'chosen_samples_score': ['0.82368296', '0.82435495', '0.82620025', '0.82679987', '0.8272156', '0.8268816', '0.82633615', '0.8288476', '0.8412038', '0.83296305', '0.868638', '0.9485597', '0.84894985', '1.0062044', '0.84919006', '0.9028626', '0.8406529', '0.89276195', '0.9119804', '0.843177', '0.9292592', '0.837822', '0.86127084', '0.85608834', '0.8820862', '0.8466215', '0.91844624', '0.83888566', '0.8303275', '0.85026795', '0.93115014', '0.9235748', '0.9010499', '0.83418304', '0.8748658', '0.8348219', '0.91408736', '0.86375386', '0.83385', '0.87786657']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9437, 'nll': 0.4243698564529419}, 'chosen_samples': [32513, 36141, 27653, 52210, 42793, 54520, 34739, 32445, 47463, 31293, 40712, 34220, 13428, 2014, 2381, 40487, 54858, 52878, 22229, 50916, 19430, 45666, 57018, 8458, 42078, 19188, 24462, 59314, 35126, 42715, 39561, 5891, 18018, 5124, 3456, 13970, 7549, 49153, 4822, 13677], 'chosen_samples_score': ['0.88432956', '0.8867369', '0.89116913', '0.8899983', '0.8905566', '0.8877046', '0.8923515', '0.8935921', '0.8942262', '0.8945992', '0.93953085', '0.97593504', '0.9200697', '0.8990247', '1.0103815', '0.9784751', '0.9413226', '0.91395396', '0.92357224', '0.9013147', '0.9318217', '0.9614289', '0.938019', '0.9758568', '1.0776334', '0.9356046', '0.9126134', '0.98482525', '0.89599276', '0.9668826', '0.8995188', '0.9311394', '0.9784512', '0.89545846', '0.90657645', '0.9322274', '0.9466175', '0.99423945', '0.9041354', '0.89712155']})
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store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9497, 'nll': 0.372473957157135}, 'chosen_samples': [16768, 19814, 47247, 27596, 12934, 18130, 57728, 58024, 7190, 34328, 41924, 22595, 55330, 57718, 31046, 9290, 21270, 48360, 43961, 8268, 55739, 31845, 57665, 6903, 42472, 11569, 45446, 8480, 15771, 1075, 1674, 4797, 903, 43941, 31738, 57334, 4784, 41371, 36704, 29109], 'chosen_samples_score': ['0.8512092', '0.8515949', '0.8521107', '0.85332227', '0.8662715', '0.8601681', '0.8721654', '0.8873103', '0.86332434', '0.88413817', '0.8813315', '0.862488', '0.8747829', '0.88072747', '0.8688696', '0.884111', '0.8602413', '0.85604763', '0.85913754', '0.86111003', '0.8655915', '0.8934767', '0.9407845', '0.8985398', '0.9433646', '0.9003738', '1.0020521', '0.90832144', '0.9206123', '0.9174824', '0.9326379', '0.91792583', '0.9327108', '0.9339302', '0.95070505', '0.95259285', '0.95341027', '0.9023066', '0.92860675', '0.8946348']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9591, 'nll': 0.3545409225463867}, 'chosen_samples': [8918, 32364, 48638, 12301, 52688, 8301, 20641, 48507, 50840, 47828, 42428, 37060, 27930, 45580, 49658, 44494, 36686, 5315, 34839, 22139, 46269, 49910, 5679, 40208, 50308, 12305, 36126, 29832, 28536, 39516, 37004, 37050, 28512, 49517, 52516, 32776, 48649, 18487, 28392, 42784], 'chosen_samples_score': ['0.94416183', '0.94510984', '0.94821334', '0.95011777', '0.952885', '0.9466496', '0.9531402', '0.9572917', '0.9604722', '0.96130395', '0.96273357', '0.9620564', '0.96668875', '0.9848403', '1.207676', '0.98368245', '1.09063', '0.9737703', '1.0282121', '1.0010078', '1.0231365', '0.9773249', '0.98010993', '1.0171804', '1.0146589', '0.97307503', '1.123386', '0.99348533', '1.0722594', '0.9720322', '0.97525954', '0.97941256', '1.1637771', '0.9736438', '0.96873087', '1.0136513', '1.009088', '1.0154681', '0.9909446', '0.98006177']})
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